Good evening,
I am attempting to run the following analysis on TCGA data, however
something is being reported as an error in my arguments... any ideas as to
what is incorrect in the following? Thanks!
1 library(TCGAbiolinks)
2
3 # Download the DNA methylation data: HumanMethylation450 LGG and GBM.
4 path <? "."
5
6 query.met <? TCGAquery(tumor =
c("LGG","GBM"),"HumanMethylation450",
level = 3)
7 TCGAdownload(query.met, path = path )
8 met <? TCGAprepare(query = query.met,dir = path,
9 add.subtype = TRUE, add.clinical = TRUE,
10 summarizedExperiment = TRUE,
11 save = TRUE, filename = "lgg_gbm_met.rda")
12
13 # Download the expression data: IlluminaHiSeq_RNASeqV2 LGG and GBM.
14 query.exp <? TCGAquery(tumor = c("lgg","gbm"),
platform = "IlluminaHiSeq_
RNASeqV2",level = 3)
15
16 TCGAdownload(query.exp,path = path, type = "rsem.genes.normalized_
results")
17
18 exp <? TCGAprepare(query = query.exp, dir = path,
19 summarizedExperiment = TRUE,
20 add.subtype = TRUE, add.clinical = TRUE,
21 type = "rsem.genes.normalized_results",
22 save = T,filename = "lgg_gbm_exp.rda")
To download data on DNA methylation and gene expression?
1 library(summarizedExperiment)
2 # get expression matrix
3 data <? assay(exp)
4
5 # get sample information
6 sample.info <? colData(exp)
7
8 # get genes information
9 genes.info <? rowRanges(exp)
Following stepwise procedure for obtaining GBM and LGG clinical data?
1 # get clinical patient data for GBM samples
2 gbm_clin <? TCGAquery_clinic("gbm","clinical_patient")
3
4 # get clinical patient data for LGG samples
5 lgg_clin <? TCGAquery_clinic("lgg","clinical_patient")
6
7 # Bind the results, as the columns might not be the same,
8 # we will plyr rbind.fill , to have all columns from both files
9 clinical <? plyr::rbind.fill(gbm_clin ,lgg_clin)
10
11 # Other clinical files can be downloaded,
12 # Use ?TCGAquery_clinic for more information
13 clin_radiation <?
TCGAquery_clinic("lgg","clinical_radiation")
14
15 # Also, you can get clinical information from different tumor types.
16 # For example sample 1 is GBM, sample 2 and 3 are TGCT
17 data <? TCGAquery_clinic(clinical_data_type =
"clinical_patient",
18 samples = c("TCGA-06-5416-01A-01D-1481-05",
19 "TCGA-2G-AAEW-01A-11D-A42Z-05",
20 "TCGA-2G-AAEX-01A-11D-A42Z-05"))
# Searching idat file for DNA methylation
query <- GDCquery(project = "TCGA-GBM",
data.category = "Raw microarray data",
data.type = "Raw intensities",
experimental.strategy = "Methylation array",
legacy = TRUE,
file.type = ".idat",
platform = "Illumina Human Methylation 450")
**Repeat for LGG**
To access mutational information concerning TMZ methylation?
> mutation <? TCGAquery_maf(tumor = "lgg")
2 Getting maf tables
3 Source: https://wiki.nci.nih.gov/display/TCGA/TCGA+MAF+Files
4 We found these maf files below:
5 MAF.File.Name
6 2 hgsc.bcm.edu_LGG.IlluminaGA_DNASeq.1.somatic.maf
7
8 3 LGG_FINAL_ANALYSIS.aggregated.capture.tcga.uuid.curated.somatic.maf
9
10 Archive.Name Deploy.Date
11 2 hgsc.bcm.edu_LGG.IlluminaGA_DNASeq_automated.Level_2.1.0.0
10-DEC-13
12 3 broad.mit.edu_LGG.IlluminaGA_DNASeq_curated.Level_2.1.3.0 24-DEC-14
13
14 Please, select the line that you want to download: 3
**Repeat this for GBM***
Selecting specified lines to download?
1 gbm.subtypes <? TCGAquery_subtype(tumor = "gbm")
2 lgg.subtypes <? TCGAquery_subtype(tumor = "lgg?)
Downloading data via the Bioconductor package RTCGAtoolbox?
library(RTCGAToolbox)
2
3 # Get the last run dates
4 lastRunDate <? getFirehoseRunningDates()[1]
5 lastAnalyseDate <? getFirehoseAnalyzeDates(1)
6
7 # get DNA methylation data, RNAseq2 and clinical data for LGG
8 lgg.data <? getFirehoseData(dataset = "LGG",
9 gistic2_Date = getFirehoseAnalyzeDates(1), runDate = lastRunDate,
10 Methylation = TRUE, RNAseq2_Gene_Norm = TRUE, Clinic = TRUE,
11 Mutation = T,
12 fileSizeLimit = 10000)
13
14 # get DNA methylation data, RNAseq2 and clinical data for GBM
15 gbm.data <? getFirehoseData(dataset = "GBM",
16 runDate = lastDate, gistic2_Date = getFirehoseAnalyzeDates(1),
17 Methylation = TRUE, Clinic = TRUE, RNAseq2_Gene_Norm = TRUE,
18 fileSizeLimit = 10000)
19
20 # To access the data you should use the getData function
21 # or simply access with @ (for example gbm.data at Clinical)
22 gbm.mut <? getData(gbm.data,"Mutations")
23 gbm.clin <? getData(gbm.data,"Clinical")
24 gbm.gistic <? getData(gbm.data,"GISTIC")
Genomic Analysis/Final data extraction:
Enable ?getData? to access the data
Obtaining GISTIC results?
1 # Download GISTIC results
2 gistic <? getFirehoseData("GBM",gistic2_Date
="20141017" )
3
4 # get GISTIC results
5 gistic.allbygene <? gistic at GISTIC@AllByGene
6 gistic.thresholedbygene <? gistic at GISTIC@ThresholedByGene
Repeat this procedure to obtain LGG GISTIC results.
***Please ignore the 'non-coded' text as they are procedural
steps/classifications***
[[alternative HTML version deleted]]
You should probably post this on the Bioconductor list rather then here, as you would more likely find the expertise you seek there. You are using Bioconductor packages after all. https://support.bioconductor.org/ Cheers, Bert On Sun, Aug 26, 2018 at 2:09 PM Spencer Brackett < spbrackett20 at saintjosephhs.com> wrote:> Good evening, > > I am attempting to run the following analysis on TCGA data, however > something is being reported as an error in my arguments... any ideas as to > what is incorrect in the following? Thanks! > > 1 library(TCGAbiolinks) > 2 > 3 # Download the DNA methylation data: HumanMethylation450 LGG and GBM. > 4 path <? "." > 5 > 6 query.met <? TCGAquery(tumor = c("LGG","GBM"),"HumanMethylation450", > level = 3) > 7 TCGAdownload(query.met, path = path ) > 8 met <? TCGAprepare(query = query.met,dir = path, > 9 add.subtype = TRUE, add.clinical = TRUE, > 10 summarizedExperiment = TRUE, > 11 save = TRUE, filename = "lgg_gbm_met.rda") > 12 > 13 # Download the expression data: IlluminaHiSeq_RNASeqV2 LGG and GBM. > 14 query.exp <? TCGAquery(tumor = c("lgg","gbm"), platform > "IlluminaHiSeq_ > RNASeqV2",level = 3) > 15 > 16 TCGAdownload(query.exp,path = path, type = "rsem.genes.normalized_ > results") > 17 > 18 exp <? TCGAprepare(query = query.exp, dir = path, > 19 summarizedExperiment = TRUE, > 20 add.subtype = TRUE, add.clinical = TRUE, > 21 type = "rsem.genes.normalized_results", > 22 save = T,filename = "lgg_gbm_exp.rda") > > To download data on DNA methylation and gene expression? > > 1 library(summarizedExperiment) > 2 # get expression matrix > 3 data <? assay(exp) > 4 > 5 # get sample information > 6 sample.info <? colData(exp) > 7 > 8 # get genes information > 9 genes.info <? rowRanges(exp) > > Following stepwise procedure for obtaining GBM and LGG clinical data? > > 1 # get clinical patient data for GBM samples > 2 gbm_clin <? TCGAquery_clinic("gbm","clinical_patient") > 3 > 4 # get clinical patient data for LGG samples > 5 lgg_clin <? TCGAquery_clinic("lgg","clinical_patient") > 6 > 7 # Bind the results, as the columns might not be the same, > 8 # we will plyr rbind.fill , to have all columns from both files > 9 clinical <? plyr::rbind.fill(gbm_clin ,lgg_clin) > 10 > 11 # Other clinical files can be downloaded, > 12 # Use ?TCGAquery_clinic for more information > 13 clin_radiation <? TCGAquery_clinic("lgg","clinical_radiation") > 14 > 15 # Also, you can get clinical information from different tumor types. > 16 # For example sample 1 is GBM, sample 2 and 3 are TGCT > 17 data <? TCGAquery_clinic(clinical_data_type = "clinical_patient", > 18 samples = c("TCGA-06-5416-01A-01D-1481-05", > 19 "TCGA-2G-AAEW-01A-11D-A42Z-05", > 20 "TCGA-2G-AAEX-01A-11D-A42Z-05")) > > > # Searching idat file for DNA methylation > query <- GDCquery(project = "TCGA-GBM", > data.category = "Raw microarray data", > data.type = "Raw intensities", > experimental.strategy = "Methylation array", > legacy = TRUE, > file.type = ".idat", > platform = "Illumina Human Methylation 450") > > **Repeat for LGG** > > To access mutational information concerning TMZ methylation? > > > mutation <? TCGAquery_maf(tumor = "lgg") > 2 Getting maf tables > 3 Source: https://wiki.nci.nih.gov/display/TCGA/TCGA+MAF+Files > 4 We found these maf files below: > 5 MAF.File.Name > 6 2 hgsc.bcm.edu_LGG.IlluminaGA_DNASeq.1.somatic.maf > 7 > 8 3 LGG_FINAL_ANALYSIS.aggregated.capture.tcga.uuid.curated.somatic.maf > 9 > 10 Archive.Name Deploy.Date > 11 2 hgsc.bcm.edu_LGG.IlluminaGA_DNASeq_automated.Level_2.1.0.0 > 10-DEC-13 > 12 3 broad.mit.edu_LGG.IlluminaGA_DNASeq_curated.Level_2.1.3.0 > 24-DEC-14 > 13 > 14 Please, select the line that you want to download: 3 > > **Repeat this for GBM*** > > Selecting specified lines to download? > > 1 gbm.subtypes <? TCGAquery_subtype(tumor = "gbm") > 2 lgg.subtypes <? TCGAquery_subtype(tumor = "lgg?) > > > > Downloading data via the Bioconductor package RTCGAtoolbox? > > library(RTCGAToolbox) > 2 > 3 # Get the last run dates > 4 lastRunDate <? getFirehoseRunningDates()[1] > 5 lastAnalyseDate <? getFirehoseAnalyzeDates(1) > 6 > 7 # get DNA methylation data, RNAseq2 and clinical data for LGG > 8 lgg.data <? getFirehoseData(dataset = "LGG", > 9 gistic2_Date = getFirehoseAnalyzeDates(1), runDate = lastRunDate, > 10 Methylation = TRUE, RNAseq2_Gene_Norm = TRUE, Clinic = TRUE, > 11 Mutation = T, > 12 fileSizeLimit = 10000) > 13 > 14 # get DNA methylation data, RNAseq2 and clinical data for GBM > 15 gbm.data <? getFirehoseData(dataset = "GBM", > 16 runDate = lastDate, gistic2_Date = getFirehoseAnalyzeDates(1), > 17 Methylation = TRUE, Clinic = TRUE, RNAseq2_Gene_Norm = TRUE, > 18 fileSizeLimit = 10000) > 19 > 20 # To access the data you should use the getData function > 21 # or simply access with @ (for example gbm.data at Clinical) > 22 gbm.mut <? getData(gbm.data,"Mutations") > 23 gbm.clin <? getData(gbm.data,"Clinical") > 24 gbm.gistic <? getData(gbm.data,"GISTIC") > > > > > > > Genomic Analysis/Final data extraction: > > Enable ?getData? to access the data > > Obtaining GISTIC results? > > 1 # Download GISTIC results > 2 gistic <? getFirehoseData("GBM",gistic2_Date ="20141017" ) > 3 > 4 # get GISTIC results > 5 gistic.allbygene <? gistic at GISTIC@AllByGene > 6 gistic.thresholedbygene <? gistic at GISTIC@ThresholedByGene > > Repeat this procedure to obtain LGG GISTIC results. > > ***Please ignore the 'non-coded' text as they are procedural > steps/classifications*** > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >[[alternative HTML version deleted]]
Thanks! Will do. On Sun, Aug 26, 2018 at 5:37 PM Bert Gunter <bgunter.4567 at gmail.com> wrote:> You should probably post this on the Bioconductor list rather then here, > as you would more likely find the expertise you seek there. You are using > Bioconductor packages after all. > > https://support.bioconductor.org/ > > Cheers, > Bert > > > On Sun, Aug 26, 2018 at 2:09 PM Spencer Brackett < > spbrackett20 at saintjosephhs.com> wrote: > >> Good evening, >> >> I am attempting to run the following analysis on TCGA data, however >> something is being reported as an error in my arguments... any ideas as to >> what is incorrect in the following? Thanks! >> >> 1 library(TCGAbiolinks) >> 2 >> 3 # Download the DNA methylation data: HumanMethylation450 LGG and GBM. >> 4 path <? "." >> 5 >> 6 query.met <? TCGAquery(tumor = c("LGG","GBM"),"HumanMethylation450", >> level = 3) >> 7 TCGAdownload(query.met, path = path ) >> 8 met <? TCGAprepare(query = query.met,dir = path, >> 9 add.subtype = TRUE, add.clinical = TRUE, >> 10 summarizedExperiment = TRUE, >> 11 save = TRUE, filename = "lgg_gbm_met.rda") >> 12 >> 13 # Download the expression data: IlluminaHiSeq_RNASeqV2 LGG and GBM. >> 14 query.exp <? TCGAquery(tumor = c("lgg","gbm"), platform >> "IlluminaHiSeq_ >> RNASeqV2",level = 3) >> 15 >> 16 TCGAdownload(query.exp,path = path, type = "rsem.genes.normalized_ >> results") >> 17 >> 18 exp <? TCGAprepare(query = query.exp, dir = path, >> 19 summarizedExperiment = TRUE, >> 20 add.subtype = TRUE, add.clinical = TRUE, >> 21 type = "rsem.genes.normalized_results", >> 22 save = T,filename = "lgg_gbm_exp.rda") >> >> To download data on DNA methylation and gene expression? >> >> 1 library(summarizedExperiment) >> 2 # get expression matrix >> 3 data <? assay(exp) >> 4 >> 5 # get sample information >> 6 sample.info <? colData(exp) >> 7 >> 8 # get genes information >> 9 genes.info <? rowRanges(exp) >> >> Following stepwise procedure for obtaining GBM and LGG clinical data? >> >> 1 # get clinical patient data for GBM samples >> 2 gbm_clin <? TCGAquery_clinic("gbm","clinical_patient") >> 3 >> 4 # get clinical patient data for LGG samples >> 5 lgg_clin <? TCGAquery_clinic("lgg","clinical_patient") >> 6 >> 7 # Bind the results, as the columns might not be the same, >> 8 # we will plyr rbind.fill , to have all columns from both files >> 9 clinical <? plyr::rbind.fill(gbm_clin ,lgg_clin) >> 10 >> 11 # Other clinical files can be downloaded, >> 12 # Use ?TCGAquery_clinic for more information >> 13 clin_radiation <? TCGAquery_clinic("lgg","clinical_radiation") >> 14 >> 15 # Also, you can get clinical information from different tumor types. >> 16 # For example sample 1 is GBM, sample 2 and 3 are TGCT >> 17 data <? TCGAquery_clinic(clinical_data_type = "clinical_patient", >> 18 samples = c("TCGA-06-5416-01A-01D-1481-05", >> 19 "TCGA-2G-AAEW-01A-11D-A42Z-05", >> 20 "TCGA-2G-AAEX-01A-11D-A42Z-05")) >> >> >> # Searching idat file for DNA methylation >> query <- GDCquery(project = "TCGA-GBM", >> data.category = "Raw microarray data", >> data.type = "Raw intensities", >> experimental.strategy = "Methylation array", >> legacy = TRUE, >> file.type = ".idat", >> platform = "Illumina Human Methylation 450") >> >> **Repeat for LGG** >> >> To access mutational information concerning TMZ methylation? >> >> > mutation <? TCGAquery_maf(tumor = "lgg") >> 2 Getting maf tables >> 3 Source: https://wiki.nci.nih.gov/display/TCGA/TCGA+MAF+Files >> 4 We found these maf files below: >> 5 MAF.File.Name >> 6 2 hgsc.bcm.edu_LGG.IlluminaGA_DNASeq.1.somatic.maf >> 7 >> 8 3 LGG_FINAL_ANALYSIS.aggregated.capture.tcga.uuid.curated.somatic.maf >> 9 >> 10 Archive.Name Deploy.Date >> 11 2 hgsc.bcm.edu_LGG.IlluminaGA_DNASeq_automated.Level_2.1.0.0 >> 10-DEC-13 >> 12 3 broad.mit.edu_LGG.IlluminaGA_DNASeq_curated.Level_2.1.3.0 >> 24-DEC-14 >> 13 >> 14 Please, select the line that you want to download: 3 >> >> **Repeat this for GBM*** >> >> Selecting specified lines to download? >> >> 1 gbm.subtypes <? TCGAquery_subtype(tumor = "gbm") >> 2 lgg.subtypes <? TCGAquery_subtype(tumor = "lgg?) >> >> >> >> Downloading data via the Bioconductor package RTCGAtoolbox? >> >> library(RTCGAToolbox) >> 2 >> 3 # Get the last run dates >> 4 lastRunDate <? getFirehoseRunningDates()[1] >> 5 lastAnalyseDate <? getFirehoseAnalyzeDates(1) >> 6 >> 7 # get DNA methylation data, RNAseq2 and clinical data for LGG >> 8 lgg.data <? getFirehoseData(dataset = "LGG", >> 9 gistic2_Date = getFirehoseAnalyzeDates(1), runDate = lastRunDate, >> 10 Methylation = TRUE, RNAseq2_Gene_Norm = TRUE, Clinic = TRUE, >> 11 Mutation = T, >> 12 fileSizeLimit = 10000) >> 13 >> 14 # get DNA methylation data, RNAseq2 and clinical data for GBM >> 15 gbm.data <? getFirehoseData(dataset = "GBM", >> 16 runDate = lastDate, gistic2_Date = getFirehoseAnalyzeDates(1), >> 17 Methylation = TRUE, Clinic = TRUE, RNAseq2_Gene_Norm = TRUE, >> 18 fileSizeLimit = 10000) >> 19 >> 20 # To access the data you should use the getData function >> 21 # or simply access with @ (for example gbm.data at Clinical) >> 22 gbm.mut <? getData(gbm.data,"Mutations") >> 23 gbm.clin <? getData(gbm.data,"Clinical") >> 24 gbm.gistic <? getData(gbm.data,"GISTIC") >> >> >> >> >> >> >> Genomic Analysis/Final data extraction: >> >> Enable ?getData? to access the data >> >> Obtaining GISTIC results? >> >> 1 # Download GISTIC results >> 2 gistic <? getFirehoseData("GBM",gistic2_Date ="20141017" ) >> 3 >> 4 # get GISTIC results >> 5 gistic.allbygene <? gistic at GISTIC@AllByGene >> 6 gistic.thresholedbygene <? gistic at GISTIC@ThresholedByGene >> >> Repeat this procedure to obtain LGG GISTIC results. >> >> ***Please ignore the 'non-coded' text as they are procedural >> steps/classifications*** >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> >[[alternative HTML version deleted]]